LAPSE:2020.0096
Published Article
LAPSE:2020.0096
Optimal Siting and Sizing of Distributed Generation Based on Improved Nondominated Sorting Genetic Algorithm II
Wei Liu, Fengming Luo, Yuanhong Liu, Wei Ding
January 19, 2020
With the development of distributed generation technology, the problem of distributed generation (DG) planning become one of the important subjects. This paper proposes an Improved non-dominated sorting genetic algorithm-II (INSGA-II) for solving the optimal siting and sizing of DG units. Firstly, the multi-objective optimization model is established by considering the energy-saving benefit, line loss, and voltage deviation values. In addition, relay protection constraints are introduced on the basis of node voltage, branch current, and capacity constraints. Secondly, the violation constrained index and improved mutation operator are proposed to increase the population diversity of non-dominated sorting genetic algorithm-II (NSGA-II), and the uniformity of the solution set of the potential crowding distance improvement algorithm is introduced. In order to verify the performance of the proposed INSGA-II algorithm, NSGA-II and multiple objective particle swarm optimization algorithms are used to perform various examples in IEEE 33-, 69-, and 118-bus systems. The convergence metric and spacing metric are used as the performance evaluation criteria. Finally, static and dynamic distribution network planning with the integrated DG are performed separately. The results of the various experiments show the proposed algorithm is effective for the siting and sizing of DG units in a distribution network. Most importantly, it also can achieve desirable economic efficiency and safer voltage level.
Keywords
distributed generation (DG), INSGA-II, multi-objective optimization, potential crowding distance, static and dynamic planning
Suggested Citation
Liu W, Luo F, Liu Y, Ding W. Optimal Siting and Sizing of Distributed Generation Based on Improved Nondominated Sorting Genetic Algorithm II. (2020). LAPSE:2020.0096
Author Affiliations
Liu W: School of Electrical Information and Engineering, Northeast Petroleum University, Daqing 163318, China
Luo F: Lorentech (Beijing) Co., Ltd., Beijing 100000, China [ORCID]
Liu Y: School of Electrical Information and Engineering, Northeast Petroleum University, Daqing 163318, China; Faculty of Engineering and Environment, Northumbria University, Newcastle NE1 8ST, UK
Ding W: School of Electrical Information and Engineering, Northeast Petroleum University, Daqing 163318, China
Journal Name
Processes
Volume
7
Issue
12
Article Number
E955
Year
2019
Publication Date
2019-12-13
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr7120955, Publication Type: Journal Article
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LAPSE:2020.0096
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doi:10.3390/pr7120955
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Jan 19, 2020
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Jan 19, 2020
 
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Calvin Tsay
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